Data scientists are in high demand globally, and organizations invest significant time and money in hiring new talent. This article will give you some pointers on how to start your career as a data scientist. Every individual wishes to have the foresight to choose the ideal career and prepare for it, but real life isn’t always a straight line—and that’s part of what makes it interesting. Furthermore, new industries and opportunities are constantly emerging as a result of fast-changing technologies.
Data science is a flourishing and much-in-demand career field with promising job prospects, so now is an excellent time to consider whether it’s the right next step for you to choose data science as your career goal. The good news is that becoming a data scientist does not require any prior experience. You can learn data science skills on your own in a variety of ways.
Now the question arises that can you start your data scientist career by learning to code? The answer is definitely “Yes”, but learning to code is only a single aspect of becoming a data scientist. You have to be proficient in other technical and non-technical skills to be a data scientist.
Before we go over the skills you’ll require to become a data scientist without prior experience, let’s define what a data scientist does.
What is a data scientist?
Data scientists gather and clean enormous amounts of data, maintain user-friendly dashboards and databases, explicate data to give solutions, run experiments, create algorithms, and present data to clients in visually appealing and comprehensible forms.
There are numerous reasons to choose data science as your career field, which are:
- High salary
- Stability and growth in the job market, even during the pandemic
- Amazing perks
- Interesting problems to solve across a wide range of industries
Is data science challenging to learn?
Whether data science is complex or not is primarily determined by your background and preference to work with numbers and data. While data scientists does not require the same software engineering or machine learning level as data engineers, you will need to code to create predictive models.
Getting expertise in the stream of data science is not child’s play. It involves challenging problems, huge data, and proficiency in the technical domain, but fortunately, many free online resources help you get started as an entry-level data scientist.
Will you require a degree to become a data scientist?
Data science can be learned without a master’s degree or even a bachelor’s degree. However, most job postings require a master’s or Ph.D. in engineering, computer science, mathematics, or statistics, the demand for data scientists far outnumbers supply, meaning employers are willing to hire non-traditional candidates. Top companies like Google, Apple, and IBM, no longer require applicants to have a college diploma.
If you don’t have a degree and want to get into data science, you can study online courses and certification programs. Market these days is flooded with a plethora of courses for data science. You can choose online certification as per your choice and requirements.
The main steps for getting into data science without prior experience are outlined below.
Brush up on your mathematics abilities:
If you have a quantitative backdrop, then data science should be a natural fit for you. Before using high-tech tools to analyze data, you must first master the basics of data analysis, including plotting data points on graphs along the X and Y axes and identifying correlations. To ensure you can do structured coding and reach precise results, you have to be proficient in below listed mathematics concepts:
- Statistical methods and probability theory
- Probability distributions
- Multivariable calculus
- Linear algebra
- Hypothesis testing
- Statistical modeling and fitting
- Data summaries and descriptive statistics
- Regression analysis
- Bayesian thinking and modeling
- Markov chains
Learn one or two programming languages:
Data science is about how you can use your knowledge practically and how well you can demonstrate your relevant skills. You should be proficient in your skillset as it will make you stand ahead of the crowd in the interviews. Once you’ve mastered math, you can start learning SQL, R, Python, and SAS, all essential programming languages for aspiring data scientists. This article presents a brief overview of the skills and languages you’ll need as a data scientist, to focus on.
- Python is a scripting language with libraries for manipulating, filtering, and transforming large amounts of unstructured data. It is the tool that data scientists use the most. Deep learning, web development, machine learning, and software development are all possible with Python.
- R is a free programming language that can be used to perform complex mathematical and statistical calculations. It also has data visualization capabilities and a large support community to help you get started.
- SQL is a relational database management system that allows you to enquire and join data from multiple tables and databases.
Digital Education Platforms such as Great Learning’s data analysis course allow you to practice basic programming skills before moving to more advanced programs.
Practical Implementation:
Organizations want to see practical experience when hiring for required positions. Data scientist courses will be of great help in this situation. A good data scientist course always offers you hands-on projects which put your knowledge into practical implementation. You can put your skills to use in real-world problems and get immediate feedback as you learn more. Without experience, it can be difficult to gain knowledge. Still, by supporting online communities and starting small, you can demonstrate that you have what it takes to turn data science knowledge into measurable business outcomes.
Begin your career as a data analyst:
Data scientists and data analysts are not the same, although they are both growing in popularity. Data analysts are in control of data collection and identifying trends in datasets. Data scientists don’t just interpret data. Instead, they also use coding and mathematical modeling skills. As a first job, data analyst positions can be easier to come by and serve as a great advancement for a career in data science.
Strong Networking:
Having a strong network with other data scientists is the best way to learn more about various career options and possibly meet potential team members. You can also learn about the types of companies suitable for you, what projects you’re interested in, and how to get ready for the application process for a job.
Another excellent option is to transition into data science from another position within your organization. You can promptly start networking within your company and look into the possibility of interviewing with a data science team, assuming you meet the technical requirements.
Work on your communication skills:
In data science jobs, people don’t usually relate communication skills with rejection. They think that they will clear the interview if they are technically proficient. This is a fabrication. When working in the field, communication skills are essential. You should communicate effectively to share your ideas with a colleague or make your point in a meeting.
Inform potential employers about your career change:
As data science is such a diverse field, it is unlikely that all prior knowledge will be lost. Data scientists must be able to link their models to specific business outcomes. Although your resume and cover letter should emphasize your data science experience, you should also mention previous roles in which you used Microsoft Excel or developed business, communication, collaboration, and other transferable skills.
Make sure to add a brief section on your resume for your job transitions, highlight courses and technical languages you’ve done, and any project work you’ve implemented practically, and last but not least, your data science skill-set and business analytics courses in the best way when applying for data scientists positions without any prior experience.
Final Word:
Your data science adventure is just getting started! In data science, there is so much to learn that mastering it would take a lifetime. Remember, you don’t have to know everything to start data science; all you have to do is start! So if you want to be a data scientist, don’t give a second thought to your thinking and start preparing for it in the right direction.
